Combinatorics

Pattern recognition (psychology)

Polymer chemistry

Inference

Mathematics

Kernel (algebra)

Support vector machine

Data mining

Machine learning

Kernel method

Artificial intelligence

Chemistry

Computer science

Modal

Classifier (UML)

Emergence of numerous modalities for data generation necessitates the development of machine learning techniques that can perform efficient inference with multi-modal data. In this paper, we present an approach to learn discriminant low-dimensional projections from supervised multi-modal data. We construct intra- and inter-class similarity graphs for each modality and optimize for consensus projections in the kernel space. Features obtained with these projections can then be used to train a classifier for consensus inference. We also provide methods for out-of-sample extensions with novel test data. Classification results with standard multi-modal data sets demonstrate the efficacy of our method.

Consensus inference with multilayer graphs for multi-modal data